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Batch sequential design to achieve predictive maturity with calibrated computer models

Brian J. Williams, Jason L. Loeppky, Leslie M. Moore and Mason S. Macklem

Reliability Engineering and System Safety, 2011, vol. 96, issue 9, 1208-1219

Abstract: Sequential experiment design strategies have been proposed for efficiently augmenting initial designs to solve many problems of interest to computer experimenters, including optimization, contour and threshold estimation, and global prediction. We focus on batch sequential design strategies for achieving maturity in global prediction of discrepancy inferred from computer model calibration. Predictive maturity focuses on adding field experiments to efficiently improve discrepancy inference. Several design criteria are extended to allow batch augmentation, including integrated and maximum mean square error, maximum entropy, and two expected improvement criteria. In addition, batch versions of maximin distance and weighted distance criteria are developed. Two batch optimization algorithms are considered: modified Fedorov exchange and a binning methodology motivated by optimizing augmented fractional factorial skeleton designs.

Keywords: Computer experiment; Gaussian process; Calibration; Sequential experiment design; Expected improvement; Maximin distance; Entropy (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:96:y:2011:i:9:p:1208-1219

DOI: 10.1016/j.ress.2010.04.017

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